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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：Phys. Rev. A
摘要：Learning low-dimensional representation is a crucial issue for many machine-learning tasks, such as pattern recognition and image retrieval. In this article, we present a quantum algorithm and a quantum circuit to efficiently perform A-optimal projection for dimensionality reduction. Compared with the best-know classical algorithms, the quantum A-optimal projection (QAOP) algorithm shows an exponential speedup in both the original feature space dimension n and the reduced feature space dimension k. We show that the space and time complexity of the QAOP circuit are O[log2(nk/ϵ)] and O[log2(nk)poly(log2ϵ-1)], respectively, with fidelity at least 1-ϵ. First, a reformation of the original QAOP algorithm is proposed to help omit the quantum-classical interactions during the QAOP algorithm. Then the quantum algorithm and quantum circuit with performance guarantees are proposed. Specifically, the quantum circuit modules for preparing the initial quantum state and implementing the controlled rotation can be also used for other quantum machine-learning algorithms. © 2019 American Physical Society.
ISSN号：2469-9926
是否译文：否
发表时间：2019-03-11
合写作者：Duan, Bojia,许娟,李丹
通讯作者：袁家斌